ment profile: peru (2013) · 2018-04-25 · 17.1 madagascar 5.7 el salvador 32.4 congo rep 21.4...

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Less thanprimary Primary Secondary University 0 20 40 60 80 100 % 63.4 42.1 30.5 36.7 17.8 21.4 17.9 11.4 7.4 7.3 6.9 1.5 11.4 29.2 44.6 50.4 Labor force status by education attainment, not in school, ages 15-29 (%) Graph shows the percentage of youth who are not currently in school, and who are either in salaried employment, non-salaried employment, unem- ployed and looking for work, or not in school and not in the labor force. Status Inactivity Unemployment Employment Salaried employment Youth EducaƟon and Employment Profiles, produced by FHI 360's EducaƟon Policy and Data Center, present educaƟon and employment characterisƟcs of youth across countries. Data are drawn fr omSchool to Work TransiƟon Surveys (SWTS), which were carried out in 32 countries between 2013 and 2015. Funded by the Mastercard FoundaƟon and supported by the InternaƟonal Labour OrganizaƟon (ILO), they paint a comprehensive picture of youth employment, educaƟonal at- tainment, and schooling across countries. These profiles are designed for students, researchers and pracƟƟoners who seek country-level snapshots of youth educa- Ɵon and employment condiƟons, and were developed in consultaƟon with FHI 360 programstaff. The educaƟon and employment categories presented in the profiles are computed by EPDC analysts, and sub-divide employment into salaried and unsalaried, based upon whether respondents report earning a wage. A respondent is considered un- employed if they are not working but are acƟvely seeking work, while they are in- acƟve if they are not in school and not in the labor force. EducaƟon categories are determined based upon a respondent's highest level completed. In some cases, it is necessary to collapse educaƟon levels into binary categories (i.e. lowversus high), based on the distribuƟon of educaƟonal aƩainment within a country. So- cioeconomic status is proxied by parent's educaƟonal aƩainment. This Venn diagramdisplays schooling and employment categories for the populaƟon of 15-29 year olds in the country. The circles showthe total populaƟon for each category, and the overlapping porƟons showthe populaƟon in both categories (for example, both in school and employed). In the upper leŌ hand corner is the percentage of 15-29 year olds who are not in school and not in the labor force. YouthEducationandEmployment Profile: Peru(2013)

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Page 1: ment Profile: Peru (2013) · 2018-04-25 · 17.1 Madagascar 5.7 El Salvador 32.4 Congo Rep 21.4 Tanzania 29.0 Zambia 21.5 Malawi 12.1 Liberia 14.2 Benin 16.9 Brazil 23.2 Peru 29.1

Less than primary Primary Secondary University0

20

40

60

80

100

%

63.4

42.1

30.536.7

17.8

21.4

17.911.4

7.4

7.3

6.91.5

11.4

29.2

44.650.4

Labor force status by education attainment, not in school, ages 15-29 (%)

Graph shows the percentage of youth who are not currently in school, and who are either in salaried employment, non-salaried employment, unem-ployed and looking for work, or not in school and not in the labor force.

StatusInactivity Unemployment Employment Salaried employment

Youth Educaon and Employment Profiles, produced by FHI 360's Educaon Policyand Data Center, present educaon and employment characteriscs of youthacross countries. Data are drawn from School to Work Transion Surveys (SWTS),which were carried out in 32 countries between 2013 and 2015. Funded by theMastercard Foundaon and supported by the Internaonal Labour Organizaon(ILO), they paint a comprehensive picture of youth employment, educaonal at-tainment, and schooling across countries. These profiles are designed for students,researchers and prac oners who seek country-level snapshots of youth educa-on and employment condions, and were developed in consultaon with FHI 360program staff. The educaon and employment categories presented in the profiles are computedby EPDC analysts, and sub-divide employment into salaried and unsalaried, basedupon whether respondents report earning a wage. A respondent is considered un-employed if they are not working but are acvely seeking work, while they are in-acve if they are not in school and not in the labor force. Educaon categories aredetermined based upon a respondent's highest level completed. In some cases, itis necessary to collapse educaon levels into binary categories (i.e. low versushigh), based on the distribuon of educaonal aainment within a country. So-cioeconomic status is proxied by parent's educaonal aainment.

This Venn diagram displays schooling and employment categoriesfor the populaon of 15-29 year olds in the country. The circlesshow the total populaon for each category, and the overlappingporons show the populaon in both categories (for example,both in school and employed). In the upper le hand corner is thepercentage of 15-29 year olds who are not in school and not in

the labor force.

Youth Education and Employment Profile: Peru (2013)

Page 2: ment Profile: Peru (2013) · 2018-04-25 · 17.1 Madagascar 5.7 El Salvador 32.4 Congo Rep 21.4 Tanzania 29.0 Zambia 21.5 Malawi 12.1 Liberia 14.2 Benin 16.9 Brazil 23.2 Peru 29.1

Education

0 10 20 30 40 50

Months

Primary

Secondary

University

26

16

12

Transion length in months, by educaon aainment (#)

Length of me in months between finishing school and starng first job. Thosewho started first job before finishing schooling are excluded from the calcula-on.

Age

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0

50

100

150

200

250

300

350

400

450

500

550

Median Monthly Wage (Converted to 2013 USD PPP)

Earnings by age and educaon aainment

Graph shows the median monthly wage in 2013 US Dollars (PPP) by single year ages, for different levels of educaon. Note that single-age esmates may lack pre-cision.

Lebanon14.3

Jordan30.9

Dominican Republic17.1

Madagascar5.7

El Salvador32.4

Congo Rep21.4

Tanzania29.0

Zambia21.5

Malawi12.1

Liberia14.2

Benin16.9

Brazil23.2Peru

29.1

Youth ages 15-29, jobless (%)

Map shows countries shaded by the percentage of youth who are neither ineducaon nor employed, with darker areas indicang a higher percentage.Note that data availability is restricted to countries that parcipated in theILO's SWTS program, and have comparable data.

EducationPrimary or less Secondary or higher

Page 3: ment Profile: Peru (2013) · 2018-04-25 · 17.1 Madagascar 5.7 El Salvador 32.4 Congo Rep 21.4 Tanzania 29.0 Zambia 21.5 Malawi 12.1 Liberia 14.2 Benin 16.9 Brazil 23.2 Peru 29.1

Female Male

Primary or less Secondary orhigher

Primary or less Secondary orhigher

0

10

20

30

40

50

60

70

80

90

100

%

40.5

22.6

18.1

14.3

7.9

6.4

33.5

56.6

51.1

39.2

23.9

20.7

6.6

6.9

18.5

33.3

Labor force status by educaon aainment and gender,not in school, ages 15-29 (%)

Graph shows the percentage of youth who are not currently in school, andwho are either in salaried employment, non-salaried employment, unem-ployed and looking for work, or inacve and not in the labor force.

Sex Education0 5 10 15 20 25 30

0 5 10 15 20 25 30Months

Female Primary orless

Secondaryor higher

Male Primary orless

Secondaryor higher

29

17

26

14

Transion length in months, by educaon aainment andgender (#)

Length of me in months between finishing school and starng first job. Thosewho started first job before finishing schooling are excluded from the calculaon.

StatusInactivityUnemployment

EmploymentSalaried employment

Age

15 16 17 18 19 20 21 22 23 24 25 26 27 28 290

200

400

600

800

Median Monthly Wage (Converted to 2013 USD PPP)

Earnings by age and educaon aainment, Female

Graph shows the median monthly wage in 2013 US Dollars (PPP) by single yearages, for different levels of educaon. Note that single-age esmates may lackprecision.

EducationPrimary or less Secondary or higher

Gender

Age

15 16 17 18 19 20 21 22 23 24 25 26 27 28 290

200

400

600

800

Earnings by age and educaon aainment, Male

Graph shows the median monthly wage in 2013 US Dollars (PPP) by single yearages, for different levels of educaon. Note that single-age esmates may lack pre-cision.

EducationPrimary or less Secondary or higher

Page 4: ment Profile: Peru (2013) · 2018-04-25 · 17.1 Madagascar 5.7 El Salvador 32.4 Congo Rep 21.4 Tanzania 29.0 Zambia 21.5 Malawi 12.1 Liberia 14.2 Benin 16.9 Brazil 23.2 Peru 29.1

Low SES High SES

Primary or less Secondary orhigher

Primary or less Secondary orhigher

0

20

40

60

80

100

%

45.1

31.9

23.1

18.7

6.6

7.0

25.2

42.4

46.3

28.7

18.3

14.2

7.4

7.4

27.9

49.7

Labor force status by educaon aainment and SES, notin school, ages 15-29 (%)

Graph shows the percentage of youth who are not currently in school, and whoare either in salaried employment, non-salaried employment, unemployed andlooking for work, or inacve and not in the labor force. Socioeconomic status isproxied by parent's educaonal level, and is dependent upon the distribuonof parent's educaonal aainment within the country.

SESRespondenteducation

0 5 10 15 20 25 30

0 5 10 15 20 25 30 35

Months

Low SES Primary orless

Secondaryor higher

High SES Primary orless

Secondaryor higher

31

14

20

15

Transion length in months, by educaon aainmentand SES (#)

Length of me in months between finishing school and starng first job. Thosewho started first job before finishing schooling are excluded from the calculaon.

StatusInactivityUnemployment

EmploymentSalaried employment

Age

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0

200

400

600

800

Median Monthly Wage (Converted to 2013 USD PPP)

Earnings by age, and educaon aainment, Low SES

Graph shows the median monthly wage in 2013 US Dollars (PPP) by single yearages, for different levels of educaon. Note that single-age esmates may lackprecision.

EducationPrimary or less Secondary or higher

Socioeconomic Status

Age

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0

200

400

600

800

Earnings by age and educaon aainment, High SES

Graph shows the median monthly wage in 2013 US Dollars (PPP) by single yearages, for different levels of educaon. Note that single-age esmates may lackprecision.

EducationPrimary or less Secondary or higher